Isotropic reconstruction for electron tomography with deep learning

Yun-Tao Liu; Heng Zhang; Hui Wang; Chang-Lu Tao; Guo-Qiang Bi; Z. Hong Zhou

doi:10.1038/s41467-022-33957-8

Nature Communications (Oct 2022)

Isotropic reconstruction for electron tomography with deep learning

Yun-Tao Liu,
Heng Zhang,
Hui Wang,
Chang-Lu Tao,
Guo-Qiang Bi,
Z. Hong Zhou

Affiliations

Yun-Tao Liu: Center for Integrative Imaging, Hefei National Research Center for Physical Sciences at the Microscale, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China
Heng Zhang: Center for Integrative Imaging, Hefei National Research Center for Physical Sciences at the Microscale, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China
Hui Wang: California NanoSystems Institute, University of California, Los Angeles (UCLA)
Chang-Lu Tao: Center for Integrative Imaging, Hefei National Research Center for Physical Sciences at the Microscale, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China
Guo-Qiang Bi: Center for Integrative Imaging, Hefei National Research Center for Physical Sciences at the Microscale, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China
Z. Hong Zhou: California NanoSystems Institute, University of California, Los Angeles (UCLA)

DOI: https://doi.org/10.1038/s41467-022-33957-8
Journal volume & issue: Vol. 13, no. 1
pp. 1 – 17

Abstract

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Cryogenic electron tomography suffers from anisotropic resolution due to the missing-wedge problem. Here, the authors present IsoNet, a neural network that learn the feature representation from similar structures in the tomogram and recover the missing information for isotropic tomogram reconstruction.

Published in Nature Communications

ISSN: 2041-1723 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/ncomms/

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